Difference between revisions of "DeepLens - deep learning enabled video camera"
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| − | == Integrated Components == | + | == Integrated Components/Technologies == |
* [[Lex]] - conversational interfaces using voice and text | * [[Lex]] - conversational interfaces using voice and text | ||
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* [[Management Console]] - manage web services | * [[Management Console]] - manage web services | ||
* [[Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI]] | * [[Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI]] | ||
| + | * [http://en.wikipedia.org/wiki/SoftAP SoftAP] | ||
== Frameworks == | == Frameworks == | ||
Revision as of 06:44, 15 June 2018
Contents
Integrated Components/Technologies
- Lex - conversational interfaces using voice and text
- SageMaker - build, train, and deploy
- Polly - text to speech
- Rekognition - video analysis service
- Kinesis - collect, process, and analyze real-time, streaming data
- Lambda - run code without managing servers
- Greengrass - connected devices can run AWS Lambda functions, keep device data in sync
- Intel® Compute Library for Deep Neural Networks (clDNN) & OpenVINO - deep learning primitives for computer vision
- Simple Queue Service (SQS) - message queuing
- Simple Notification Service (SNS) - pub/sub messaging and mobile notifications
- Internet of Things (IoT) Core - process and route those messages to AWS endpoints
- DynamoDB - NoSQL database
- Simple Storage Service (S3) - object storage
- Management Console - manage web services
- Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI
- SoftAP
Frameworks
I just got my DeepLens!
Learn More
Projects
- aws-ml-vision-end2end - Jupyter Notebook tutorials walking through deep learning Frameworks (MXNet, Gluon) to Platforms (SageMaker, DeepLens) for common CV use-cases | GitHub
- Deploy Gluon models to AWS DeepLens using a simple Python API
Processor
With over 100 GFLOPS of compute power on the device, it can process deep learning predictions on HD video for real time.
